The Importance of Observability in Creating Value from Data
Almost every device and service in our lives creates information. In this tsunami of data it is easy to get mired up and lose sight of what really counts and miss the opportunities therein. It can feel that data is impossible to tackle and make sense out of - the bogeyman called Data just grows and grows.
Fortunately there is a way forward: working in tandem with AI.
A little on Observability
Observability as a term has been popular since 2018: amidst all the hype, it’s a little too easy to find yourself talking about observability without really wondering how it differs from monitoring. Yes, the latter is a verb and the former a noun, but what they represent is actually quite different. There is a lot written about the topic, so we won’t be focusing on epistemology here.
What we really want to understand is what mindset observability forms the cornerstone of. It’s about good practice, intelligence, and resiliency. Increased complexity of the modern infrastructure leads to an undisputed need for better monitoring higher in the stack, and deeper in the system.
So what are the top things to remember about observability?
Step one: ensure you’re getting the data you actually need in the first place. Next, configure alerting to flag everything that matters. With incidents and performance issues flagged and logged, it’s time to investigate inconsistencies and issues.
Effective observability in software gives enterprises the ability to explore, analyse and understand any state a system finds itself in regardless of where a friction point occurs - all without having to deploy new code, nor adding new log lines or metrics, to troubleshoot. The term originates from control theory, where observability is defined as “a measure of how well internal states of a system can be inferred by knowledge of its external outputs.” Observability is about understanding a system’s various states, when working or when down, from the data the system is already giving you.
So why do we increasingly need to consider observability?
Enterprise grade tools need to deliver observability by default, particularly as system complexity is outpacing an organisations’ ability to predict when something might break.
While software is becoming all the more complex, the infrastructure powering it is seeing the convergence of pressures from microservices and their required persistence, and the containers that deconstruct legacy monoliths into their own complex systems. This makes for a fantastic dynamic for launching new products, but a more distributed challenge for those deploying them.
The number of products and applications we use is growing exponentially: with every cool new software on the radar, comes an increasing number of platforms and tools to integrate with, and each of these requires substantial monitoring in order to. This explosion of innovation to work with is fantastic for builders, but it creates an increasingly diverse set of data points to monitor efficiently.
When environments are complex and distributed, setting up basic monitoring for expected issues might not catch new problems that arise in interconnected systems. Some new challenges fall under the category of “unknown unknowns” because they haven’t previously been picked up on and monitored. Sometimes the source of these is unknown, which is why highly observable systems are key to discovering problems before you know you have them.
All of this assumes you are capturing the correct data in the first instance.
In order to improve software observability, enterprises need to capture telemetry data with reliable runtime logs, while also having the ability to query data dynamically to deliver business intelligence. The ability to interact with the data captured is the next step in observability delivering enterprise value.
Challenging the Status Quo
The introduction of AI into businesses’ data analytics operations first and foremost enable the people to work smarter, and for the business to truly and properly enhance its operations. For businesses, regardless of the industry they’re in, this translates into such desirables as New Business, Cut costs and Streamlining lead times. While the data team has to monitor the data operations to capitalise on the discovered insights, the AI will take care of the everyday.
The old way for businesses to look at their any IT operation is through work years, meaning that any given task has to have a dedicated person working on it. Fortunately, we are moving away from this thinking into something much, much better and smarter - for both the people and the companies themselves.
The RAIN platform works by ingesting data from multiple sources, and then making it available for you to interact with - in real time. Instead of being scared of breaking data flows, the platform enables enterprises to ship more often, because their ability to find and solve problems is supercharged by visibility across all data running through the RAIN platform. The insights observability delivers is fed back into an enterprises’ development process, in order to make better decisions about what to build, when to ship it and how to maintain it.
When deployed across Ori Global Cloud, the orchestration platform simplifies workload provisioning across public, hybrid and multi-cloud infrastructures to support countless enterprise and consumer use cases. This enables global rollouts of distributed cloud services to any infrastructure, without timely configurations and refactoring for hybrid deployments.
These capabilities can monitor the systems analysing data locally, in order to predict when a system is likely to fail. Identification and repair of that machinery before failure saves valuable time and costs on all vectors. The most effective AI solutions can be seamlessly integrated into existing cloud architecture and data flows.
Genuine automation and problem prevention begins with embracing observability in order to get the most out of data.
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